Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Model d'Autoregressió Vectorial (VAR)× | Model d'ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Camp | Econometria | Econometria |
| Família | Regression model | Regression model |
| Any d'origen≠ | 2005 | 2015 |
| Autor original≠ | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition | Box & Jenkins (Box-Jenkins methodology) |
| Tipus≠ | Multivariate time-series model | Univariate time-series model |
| Font seminal≠ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 |
| Àlies≠ | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Relacionats≠ | 4 | 5 |
| Resum≠ | Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005). | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). |
| ScholarGateConjunt de dades ↗ |
|
|